Stratified Sampling Design Based on Data Mining

김연국; 오윤환; 박성훈; 조성준; 박하영

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자료유형: 학술저널

저자정보: 김연국 오윤환 박성훈 조성준 박하영

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제19권 제3호

발행연도: 2013.1

수록면: 186 - 195 (10page)

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Objectives: To explore classification rules based on data mining methodologies which are to be used in defining strata in stratified sampling of healthcare providers with improved sampling efficiency. Methods: We performed k-means clustering to group providers with similar characteristics, then, constructed decision trees on cluster labels to generate stratification rules. We assessed the variance explained by the stratification proposed in this study and by conventional stratification to evaluate the performance of the sampling design. We constructed a study database from health insurance claims data and providers’ profile data made available to this study by the Health Insurance Review and Assessment Service of South Korea, and population data from Statistics Korea. From our database, we used the data for single specialty clinics or hospitals in two specialties, general surgery and ophthalmology, for the year 2011 in this study. Results: Data mining resulted in five strata in general surgery with two stratification variables, the number of inpatients per specialist and population density of provider location, and five strata in ophthalmology with two stratification variables, the number of inpatients per specialist and number of beds. The percentages of variance in annual changes in the productivity of specialists explained by the stratification in general surgery and ophthalmology were 22% and 8%, respectively, whereas conventional stratification by the type of provider location and number of beds explained 2% and 0.2% of variance, respectively. Conclusions: This study demonstrated that data mining methods can be used in designing efficient stratified sampling with variables readily available to the insurer and government; it offers an alternative to the existing stratification method that is widely used in healthcare provider surveys in South Korea.

#Sampling Studies #Decision Trees #Data Mining

참고문헌 (10)

참고문헌 신청

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